An official website of the United States government
Abstract Interest in magnetic fields on the ancient Earth and other planetary bodies has motivated the paleomagnetic analysis of complex rocks such as meteorites that carry heterogeneous magnetizations at <<1 mm scales. The net magnetic moment of natural remanent magnetization (NRM) in such small samples is often below the detection threshold of common cryogenic magnetometers. The quantum diamond microscope (QDM) is an emerging magnetic imaging technology with ~1 μm resolution and can, in principle, recover magnetizations as weak as 10−17 Am2. However, the typically 1–100 μm sample‐to‐sensor distance of QDM measurements can result in complex (nondipolar) magnetic field maps, from which the net magnetic moment cannot be determined using a simple algorithm. Here we generate synthetic magnetic field maps to quantify the errors introduced by sample nondipolarity and by map processing procedures such as upward continuation. We find that inversions based on least squares dipole fits of upward continued data can recover the net moment of complex samples with <5% to 10% error for maps with signal‐to‐noise ratio (SNR) in the range typical of current generation QDMs. We validate these error estimates experimentally using comparisons between QDM maps and between QDM and SQUID microscope data, concluding that, within the limitations described here, the QDM is a robust technique for recovering the net magnetic moment of weakly magnetized samples. More sophisticated net moment fitting algorithms in the future can be combined with upward continuation methods described here to improve accuracy.
more »
« less
Improving co-registration of geoscientific imaging datasets with micro-sized marker structures on rock samples
Abstract. Polished geological samples are frequently used in geoscientific research to investigate the chemical and physical characteristics of rocks. A broad range of imaging techniques is available to analyze such samples, but when combining datasets from multiple imaging techniques, an accurate co-registration of the datasets is often challenging. In this study, we investigate this issue in the context of Micromagnetic Tomography (MMT; De Groot et al., 2018, 2021). MMT combines surface magnetometry data with computed tomography (CT) data to analyze the magnetic state of rock samples. By combining the spatial (position) and dimensional (size) information of the magnetic grains in the samples with their magnetic surface expression, the individual magnetic moments per grain can be determined. This information can be used for paleomagnetic and rock-magnetic studies. Calculating the magnetic moments of the grains strongly depends on the correct co-registration of the two datasets, which proves to be challenging. In this study, we used two test samples for the application of micro-sized marker structures, to further develop the methodology of MMT. The marker structures are applied by microlithography and Nb-sputter coating, which are standard techniques used in the semiconductor industry. We determined that the marker structure application is possible on typical MMT samples. Marker structures larger than ca. 10 µm are clearly visible under the Quantum Diamond Microscope (QDM) used for the surface magnetometry. Given a sufficient marker structure thickness, they can also be observed in the CT scans used for determining the positions and shapes of the magnetic carriers. The marker structures are useful for identifying the orientation and location of the samples during measurements and can be used for scaling and mapping of the two datasets during data processing. Nb-marker structures do not fluoresce under the QDM, which means that no magnetic interference occurs during measurements. The application procedure is time-consuming but is valuable when a sample is lacking natural marker features, it makes the data processing time in MMT significantly faster, and more precise. This method can be useful for MMT, for Quantum Diamond Microscopy in general, and for broader geological applications that require visible anchor points for sample placement or marker structures for the co-registration of multiple datasets.
more »
« less
- Award ID(s):
- 2153786
- PAR ID:
- 10655563
- Publisher / Repository:
- Copernicus Publications on behalf of the European Geosciences Union
- Date Published:
- Journal Name:
- Geoscientific Instrumentation, Methods and Data Systems
- Volume:
- 14
- Issue:
- 2
- ISSN:
- 2193-0864
- Page Range / eLocation ID:
- 325 to 334
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Recent advances in magnetic microscopy have enabled studies of geological samples whose weak and spatially nonuniform magnetizations were previously inaccessible to standard magnetometry techniques. A quantity of central importance is the net magnetic moment, which reflects the mean direction and the intensity of the magnetization states of numerous ferromagnetic crystals within a certain volume. The planar arrangement of typical magnetic microscopy measurements, which originates from measuring the field immediately above the polished surface of a sample to maximize sensitivity and spatial resolution, makes estimating net moments considerably more challenging than with spherically distributed data. In particular, spatially extended and nonuniform magnetization distributions often cannot be adequately approximated by a single magnetic dipole. To address this limitation, we developed a multipole fitting technique that can accurately estimate net moment using spherical harmonic multipole expansions computed from planar data. Given that the optimal location for the origin of such expansions is unknown beforehand and generally unconstrained, regularization of this inverse problem is critical for obtaining accurate moment estimates from noisy experimental magnetic data. We characterized the performance of the technique using synthetic sources under different conditions (noiseless data, data corrupted with simulated white noise, and data corrupted with measured instrument noise). We then validated and demonstrated the technique using superconducting quantum interference device microscopy measurements of impact melt spherules from Lonar crater, India and dusty olivine chondrules from the CO chondrite meteorite Dominion Range 08006.more » « less
-
Abstract Computational fluid dynamics (CFD) modeling of left ventricle (LV) flow combined with patient medical imaging data has shown great potential in obtaining patient-specific hemodynamics information for functional assessment of the heart. A typical model construction pipeline usually starts with segmentation of the LV by manual delineation followed by mesh generation and registration techniques using separate software tools. However, such approaches usually require significant time and human efforts in the model generation process, limiting large-scale analysis. In this study, we propose an approach toward fully automating the model generation process for CFD simulation of LV flow to significantly reduce LV CFD model generation time. Our modeling framework leverages a novel combination of techniques including deep-learning based segmentation, geometry processing, and image registration to reliably reconstruct CFD-suitable LV models with little-to-no user intervention.1 We utilized an ensemble of two-dimensional (2D) convolutional neural networks (CNNs) for automatic segmentation of cardiac structures from three-dimensional (3D) patient images and our segmentation approach outperformed recent state-of-the-art segmentation techniques when evaluated on benchmark data containing both magnetic resonance (MR) and computed tomography(CT) cardiac scans. We demonstrate that through a combination of segmentation and geometry processing, we were able to robustly create CFD-suitable LV meshes from segmentations for 78 out of 80 test cases. Although the focus on this study is on image-to-mesh generation, we demonstrate the feasibility of this framework in supporting LV hemodynamics modeling by performing CFD simulations from two representative time-resolved patient-specific image datasets.more » « less
-
Abstract Rocks are heterogeneous multiscale porous media: two rock samples with identical bulk properties can vary widely in microstructure. The advent of digital rock technology and modern 3‐D printing provides new opportunities to replicate rocks. However, the inherent trade‐off between imaging resolution and sample size limits the scales over which microstructure and macrostructure can be identified and related to each other. Here, we develop a multiscale digital rock construction strategy by combining X‐ray computed microtomography and focused‐ion beam (FIB)‐scanning electron microscope (SEM) images, and we apply the technique to a tight sandstone. The computed tomography (CT) scanning images characterize macroscale pore structures, while the FIB‐SEM images capture microscale pore textures. The FIB‐SEM images are then coupled to CT images via a template‐matching algorithm and superposition. Bulk properties, including porosity and pore and throat size distribution, can be recovered with this approach. Permeability prediction with a pore network model for the largest connected pore network are 3 orders and 1 order of magnitude greater than the bulk rock measured value using the CT‐only and the SEM‐CT coupled images, respectively.more » « less
-
Our ability to visualize and quantify the internal structures of objects via computed tomography (CT) has fundamentally transformed science. As tomographic tools have become more broadly accessible, researchers across diverse disciplines have embraced the ability to investigate the 3D structure-function relationships of an enormous array of items. Whether studying organismal biology, animal models for human health, iterative manufacturing techniques, experimental medical devices, engineering structures, geological and planetary samples, prehistoric artifacts, or fossilized organisms, computed tomography has led to extensive methodological and basic sciences advances and is now a core element in science, technology, engineering, and mathematics (STEM) research and outreach toolkits. Tomorrow's scientific progress is built upon today's innovations. In our data-rich world, this requires access not only to publications but also to supporting data. Reliance on proprietary technologies, combined with the varied objectives of diverse research groups, has resulted in a fragmented tomography-imaging landscape, one that is functional at the individual lab level yet lacks the standardization needed to support efficient and equitable exchange and reuse of data. Developing standards and pipelines for the creation of new and future data, which can also be applied to existing datasets is a challenge that becomes increasingly difficult as the amount and diversity of legacy data grows. Global networks of CT users have proved an effective approach to addressing this kind of multifaceted challenge across a range of fields. Here we describe ongoing efforts to address barriers to recently proposed FAIR (Findability, Accessibility, Interoperability, Reuse) and open science principles by assembling interested parties from research and education communities, industry, publishers, and data repositories to approach these issues jointly in a focused, efficient, and practical way. By outlining the benefits of networks, generally, and drawing on examples from efforts by the Non-Clinical Tomography Users Research Network (NoCTURN), specifically, we illustrate how standardization of data and metadata for reuse can foster interdisciplinary collaborations and create new opportunities for future-looking, large-scale data initiatives.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/gi-14-325-2025
